{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of estimated health prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimated health prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All countries: Administrative costs\n",
    "countries = ['de','dk','fr','it','nl','se','sp','us']\n",
    "euro = ['de','dk','fr','it','nl','se','sp']\n",
    "stats = ['price','iota','hosp']\n",
    "table = pd.DataFrame(index=countries,columns=stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# European Countries and health market institutions\n",
    "statd  = ['price','comp','regu','quali','DemManag','pay']\n",
    "tabled = pd.DataFrame(index=euro,columns=statd) #countries[0:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                us        de        dk        fr        it        nl  \\\n",
      "name                                                                   \n",
      "sigma     2.097183  2.097183  2.097183  2.097183  2.097183  2.097183   \n",
      "beta      0.970000  0.970000  0.970000  0.970000  0.970000  0.970000   \n",
      "phi       0.304140  0.304140  0.304140  0.304140  0.304140  0.304140   \n",
      "psi       0.168902  0.168902  0.168902  0.168902  0.168902  0.168902   \n",
      "delta_h1 -0.967380 -1.296156 -1.601068 -1.094451 -0.715889 -1.264525   \n",
      "delta_h2  3.487244  3.953905  4.273184  3.697940  3.867593  3.998342   \n",
      "eta       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
      "tfp       1.000000  1.008961  1.255176  0.923597  0.638877  1.009820   \n",
      "price     1.000000  0.846861  0.887615  0.614844  0.712935  0.698165   \n",
      "risk      0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
      "\n",
      "                se        sp  \n",
      "name                          \n",
      "sigma     2.097183  2.097183  \n",
      "beta      0.970000  0.970000  \n",
      "phi       0.304140  0.304140  \n",
      "psi       0.168902  0.168902  \n",
      "delta_h1 -1.525023 -0.005673  \n",
      "delta_h2  4.310607  3.394047  \n",
      "eta       0.000000  0.000000  \n",
      "tfp       0.788306  0.810865  \n",
      "price     0.843816  0.650287  \n",
      "risk      0.000000  0.000000  \n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle('../estimation/output/params_ref_us.pkl')\n",
    "pars = df.loc[:,'value'].to_frame()\n",
    "pars.columns = ['us']\n",
    "for c in countries:\n",
    "    df = pd.read_pickle('../estimation/output/params_ref_'+c+'.pkl')\n",
    "    pars[c] = df.loc[:,'value']\n",
    "print(pars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_health = pars.loc['price',:]\n",
    "table.loc[:,'price']= price_health"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tablep = table.sort_values('price',ascending=False)\n",
    "plt.figure()\n",
    "b = plt.bar(tablep.index,tablep['price'],color='grey')\n",
    "b[0].set_color('black')\n",
    "plt.ylabel('$p_g/p_{US}$')\n",
    "#plt.savefig('../figures_JPE/price_heatlh.eps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "AdmiCost  = np.array([ 5.5, 1.2, 7.0, 1.2, 4.2, 1.5, 3.1, 7.4]) \n",
    "AdmiCostH = np.array([ 9.00, np.nan, 8.77, np.nan, 10.85, np.nan, np.nan, 15.51]) \n",
    "table.loc[:,'iota']= AdmiCost/AdmiCost[-1]\n",
    "table.loc[:,'hosp']= AdmiCostH/AdmiCostH[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope: 0.198401    intercept: 0.370225   R-squared: 0.005866\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(table.loc[:,'price'],table.loc[:,'iota'])\n",
    "print(\"slope: %f    intercept: %f   R-squared: %f\" % (slope, intercept, r_value**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope: 0.923157    intercept: -0.017949   R-squared: 0.604215\n"
     ]
    }
   ],
   "source": [
    "slopeH, interceptH, r_valueH, p_valueH, std_errH = stats.linregress(table.loc[['de','fr','nl','us'],'price'],table.loc[['de','fr','nl','us'],'hosp'])\n",
    "print(\"slope: %f    intercept: %f   R-squared: %f\" % (slopeH, interceptH,r_valueH**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(table.loc[:,'price'],table.loc[:,'iota'],facecolors='none', edgecolors='b',s=500.0)\n",
    "plt.plot(table.loc[:,'price'], intercept + slope*table.loc[:,'price'], 'k')\n",
    "plt.ylim(0.1,1.1)\n",
    "plt.ylabel('Administrative Costs ($\\iota$)')\n",
    "plt.xlabel('health price')\n",
    "for x,y,z in zip(table.loc[:,'price'],table.loc[:,'iota'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n",
    "plt.savefig('../figures/fig2_price_admin.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(table.loc[:,'price'],table.loc[:,'hosp'],facecolors='none', edgecolors='b',s=500.0)\n",
    "plt.plot(table.loc[['de','fr','nl','us'],'price'], interceptH + slopeH*table.loc[['de','fr','nl','us'],'price'], 'k')\n",
    "plt.ylim(0.45,1.05)\n",
    "plt.ylabel('Administrative Costs ($\\iota$)')\n",
    "plt.xlabel('health price')\n",
    "for x,y,z in zip(table.loc[:,'price'],table.loc[:,'hosp'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n",
    "plt.savefig('../figures/fig2_reviewer_price_hosp.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(table.loc[:,'price'],table.loc[:,'iota'],facecolors='none', edgecolors='b',s=500.0)\n",
    "plt.plot(table.loc[:,'price'], intercept + slope*table.loc[:,'price'], 'k')\n",
    "plt.ylim(0.1,1.1)\n",
    "plt.ylabel('Administrative Costs ($\\iota$)')\n",
    "plt.xlabel('health price')\n",
    "for x,y,z in zip(table.loc[:,'price'],table.loc[:,'iota'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n",
    "plt.scatter(table.loc[:,'price'],table.loc[:,'hosp'],facecolors='none', edgecolors='r',s=500.0)\n",
    "plt.plot(table.loc[['de','fr','nl','us'],'price'], interceptH + slopeH*table.loc[['de','fr','nl','us'],'price'], 'r')\n",
    "#plt.ylim(0.45,1.05)\n",
    "plt.ylabel('Administrative Costs ($\\iota$)')\n",
    "plt.xlabel('health price')\n",
    "for x,y,z in zip(table.loc[:,'price'],table.loc[:,'hosp'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Health marlet institutions: European countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "typeReg = ['Physician payment',\n",
    "'Hospital payment',\n",
    "'Incentives for quality',\n",
    "'Choice among providers',\n",
    "'User choice of insurer',\n",
    "'Lever',\n",
    "'Regulation of physician supply',\n",
    "'Regulation of capital investment',\n",
    "'Regulation of price for physician services',\n",
    "'Regulation of price for hospital services',\n",
    "'Regulation of pharmaceutical price',\n",
    "'Regulation of prices charged to third-party',\n",
    "'Stringency of budget constraint',\n",
    "'Control of volume',\n",
    "'Gatekeeping',\n",
    "'Depth of basic insurance',\n",
    "'Definition of benefit basket',\n",
    "'Public health objectives',\n",
    "'Use of health technology assessment',\n",
    "'Degree of decentralisation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "RegInd = np.array([\n",
    "[4.5, 5.0, 0.0, 5.3, 6.0, 5.0, 3.6, 4.0, 3.0, 3.0, 1.0, 3.7, 2.0, 2.0, 3.0, 5.6, 4.0, 0.1, 3.6, 1.5],\n",
    "[2.5, 1.0, 0.5, 2.0, 1.0, 0.0, 4.8, 4.0, 4.0, 5.0, 1.0, 4.0, 2.0, 1.7, 6.0, 5.3, 2.5, 2.4, 6.0, 2.3],\n",
    "[4.5, 5.0, 0.5, 6.0, 2.0, 0.3, 4.8, 4.0, 4.0, 3.0, 5.0, 5.0, 2.0, 1.7, 3.0, 5.2, 5.0, 5.4, 4.0, 0.0],\n",
    "[1.0, 5.0, 1.0, 6.0, 0.0, 0.0, 4.8, 6.0, 3.0, 5.0, 5.0, 4.2, 5.0, 2.3, 6.0, 5.4, 5.0, 0.9, 3.6, 2.3],\n",
    "[5.5, 1.0, 1.5, 5.0, 4.0, 5.0, 4.8, 1.5, 2.0, 5.0, 5.0, 3.2, 2.0, 1.0, 6.0, 5.7, 5.0, 0.0, 6.0, 0.0],\n",
    "[0.5, 0.0, 1.0, 0.7, 1.0, 0.0, 3.6, 4.0, 4.0, 5.0, 5.0, 4.5, 2.0, 3.0, 6.0, 5.4, 5.0, 0.1, 4.7, 5.5],\n",
    "[0.0, 3.0, 1.2, 6.0, 0.0, 0.0, 2.4, 2.9, 4.0, 5.0, 5.0, 4.5, 6.0, 2.3, 0.0, 4.9, 2.5, 0.1, 4.0, 4.3],\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tableR = pd.DataFrame(index=countries[0:7],columns=typeReg)\n",
    "\n",
    "for ii in range(20):\n",
    "    tableR.loc[:,typeReg[ii]] = RegInd[:,ii]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Physician payment</th>\n",
       "      <th>Hospital payment</th>\n",
       "      <th>Incentives for quality</th>\n",
       "      <th>Choice among providers</th>\n",
       "      <th>User choice of insurer</th>\n",
       "      <th>Lever</th>\n",
       "      <th>Regulation of physician supply</th>\n",
       "      <th>Regulation of capital investment</th>\n",
       "      <th>Regulation of price for physician services</th>\n",
       "      <th>Regulation of price for hospital services</th>\n",
       "      <th>Regulation of pharmaceutical price</th>\n",
       "      <th>Regulation of prices charged to third-party</th>\n",
       "      <th>Stringency of budget constraint</th>\n",
       "      <th>Control of volume</th>\n",
       "      <th>Gatekeeping</th>\n",
       "      <th>Depth of basic insurance</th>\n",
       "      <th>Definition of benefit basket</th>\n",
       "      <th>Public health objectives</th>\n",
       "      <th>Use of health technology assessment</th>\n",
       "      <th>Degree of decentralisation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>de</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dk</th>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fr</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>it</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>se</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sp</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Physician payment  Hospital payment  Incentives for quality  \\\n",
       "de                4.5               5.0                     0.0   \n",
       "dk                2.5               1.0                     0.5   \n",
       "fr                4.5               5.0                     0.5   \n",
       "it                1.0               5.0                     1.0   \n",
       "nl                5.5               1.0                     1.5   \n",
       "se                0.5               0.0                     1.0   \n",
       "sp                0.0               3.0                     1.2   \n",
       "\n",
       "    Choice among providers  User choice of insurer  Lever  \\\n",
       "de                     5.3                     6.0    5.0   \n",
       "dk                     2.0                     1.0    0.0   \n",
       "fr                     6.0                     2.0    0.3   \n",
       "it                     6.0                     0.0    0.0   \n",
       "nl                     5.0                     4.0    5.0   \n",
       "se                     0.7                     1.0    0.0   \n",
       "sp                     6.0                     0.0    0.0   \n",
       "\n",
       "    Regulation of physician supply  Regulation of capital investment  \\\n",
       "de                             3.6                               4.0   \n",
       "dk                             4.8                               4.0   \n",
       "fr                             4.8                               4.0   \n",
       "it                             4.8                               6.0   \n",
       "nl                             4.8                               1.5   \n",
       "se                             3.6                               4.0   \n",
       "sp                             2.4                               2.9   \n",
       "\n",
       "    Regulation of price for physician services  \\\n",
       "de                                         3.0   \n",
       "dk                                         4.0   \n",
       "fr                                         4.0   \n",
       "it                                         3.0   \n",
       "nl                                         2.0   \n",
       "se                                         4.0   \n",
       "sp                                         4.0   \n",
       "\n",
       "    Regulation of price for hospital services  \\\n",
       "de                                        3.0   \n",
       "dk                                        5.0   \n",
       "fr                                        3.0   \n",
       "it                                        5.0   \n",
       "nl                                        5.0   \n",
       "se                                        5.0   \n",
       "sp                                        5.0   \n",
       "\n",
       "    Regulation of pharmaceutical price  \\\n",
       "de                                 1.0   \n",
       "dk                                 1.0   \n",
       "fr                                 5.0   \n",
       "it                                 5.0   \n",
       "nl                                 5.0   \n",
       "se                                 5.0   \n",
       "sp                                 5.0   \n",
       "\n",
       "    Regulation of prices charged to third-party  \\\n",
       "de                                          3.7   \n",
       "dk                                          4.0   \n",
       "fr                                          5.0   \n",
       "it                                          4.2   \n",
       "nl                                          3.2   \n",
       "se                                          4.5   \n",
       "sp                                          4.5   \n",
       "\n",
       "    Stringency of budget constraint  Control of volume  Gatekeeping  \\\n",
       "de                              2.0                2.0          3.0   \n",
       "dk                              2.0                1.7          6.0   \n",
       "fr                              2.0                1.7          3.0   \n",
       "it                              5.0                2.3          6.0   \n",
       "nl                              2.0                1.0          6.0   \n",
       "se                              2.0                3.0          6.0   \n",
       "sp                              6.0                2.3          0.0   \n",
       "\n",
       "    Depth of basic insurance  Definition of benefit basket  \\\n",
       "de                       5.6                           4.0   \n",
       "dk                       5.3                           2.5   \n",
       "fr                       5.2                           5.0   \n",
       "it                       5.4                           5.0   \n",
       "nl                       5.7                           5.0   \n",
       "se                       5.4                           5.0   \n",
       "sp                       4.9                           2.5   \n",
       "\n",
       "    Public health objectives  Use of health technology assessment  \\\n",
       "de                       0.1                                  3.6   \n",
       "dk                       2.4                                  6.0   \n",
       "fr                       5.4                                  4.0   \n",
       "it                       0.9                                  3.6   \n",
       "nl                       0.0                                  6.0   \n",
       "se                       0.1                                  4.7   \n",
       "sp                       0.1                                  4.0   \n",
       "\n",
       "    Degree of decentralisation  \n",
       "de                         1.5  \n",
       "dk                         2.3  \n",
       "fr                         0.0  \n",
       "it                         2.3  \n",
       "nl                         0.0  \n",
       "se                         5.5  \n",
       "sp                         4.3  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tableR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "tabled.loc[:,'price']  = price_health[euro]\n",
    "\n",
    "tabled.loc[:,'regu']   = np.sum(tableR.loc[:,[\n",
    "'Physician payment',\n",
    "'Hospital payment',\n",
    "'Regulation of physician supply',\n",
    "'Regulation of capital investment',\n",
    "'Regulation of price for physician services',\n",
    "'Regulation of price for hospital services',\n",
    "'Regulation of pharmaceutical price',\n",
    "'Regulation of prices charged to third-party'\n",
    "]],1)/8\n",
    "\n",
    "tabled.loc[:,'quali']  = tableR.loc[:,typeReg[2]]\n",
    "\n",
    "tabled.loc[:,'markup'] = np.array([1.01803, 0.675261, 0.310128, 0.464998, 0.372489, 0.561001,0.287985])\n",
    "\n",
    "tabled.loc[:,'DemManag'] = np.sum(tableR.loc[:,[\n",
    "#'Control of volume', # +\n",
    "#'Gatekeeping', # +\n",
    "'Definition of benefit basket',  # -\n",
    "'Public health objectives' # -\n",
    "#'Use of health technology assessment', #+\n",
    "#'Degree of decentralisation'\n",
    "]],1)/2#6\n",
    "\n",
    "tabled.loc[:,'pay'] = np.sum(tableR.loc[:,[\n",
    "'Physician payment',\n",
    "'Hospital payment'\n",
    "]],1)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Phys. pay\n",
    "comp1 = np.array([4.5, 2.5, 4.5, 1.0, 5.5, 0.5, 0.0])\n",
    "# Hosp pay.\n",
    "comp2 = np.array([5.0, 1.0, 5.0, 5.0, 1.0, 0.0, 3.0])\n",
    "# Choice among providers\n",
    "comp3 = np.array([5.3, 2.0, 6.0, 6.0, 5.0, 0.7, 6.0])\n",
    "# Choice among insurer\n",
    "comp4 = np.array([6.0, 1.0, 2.0, 0.0, 4.0, 1.0, 0.0])\n",
    "# Compet on insuarnce market\n",
    "comp5 = np.array([5.0, 0.0, 0.3, 0.0, 5.0, 0.0, 0.0])\n",
    "# degree of decentralization\n",
    "comp6 = np.array([1.5, 2.3, 0.0, 2.3, 0.0, 5.5, 4.3])\n",
    "\n",
    "tabled.loc[:,'comp'] = comp3 #(comp1+comp2+comp3+comp4+comp5)/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>comp</th>\n",
       "      <th>regu</th>\n",
       "      <th>quali</th>\n",
       "      <th>DemManag</th>\n",
       "      <th>pay</th>\n",
       "      <th>markup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>de</th>\n",
       "      <td>0.846861</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.4750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>4.75</td>\n",
       "      <td>1.018030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dk</th>\n",
       "      <td>0.887615</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.2875</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.675261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fr</th>\n",
       "      <td>0.614844</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.4125</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.20</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0.310128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>it</th>\n",
       "      <td>0.712935</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.2500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.464998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl</th>\n",
       "      <td>0.698165</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.372489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>se</th>\n",
       "      <td>0.843816</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.3250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.561001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sp</th>\n",
       "      <td>0.650287</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.3500</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.287985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  comp    regu  quali  DemManag   pay    markup\n",
       "de  0.846861   5.3  3.4750    0.0      2.05  4.75  1.018030\n",
       "dk  0.887615   2.0  3.2875    0.5      2.45  1.75  0.675261\n",
       "fr  0.614844   6.0  4.4125    0.5      5.20  4.75  0.310128\n",
       "it  0.712935   6.0  4.2500    1.0      2.95  3.00  0.464998\n",
       "nl  0.698165   5.0  3.5000    1.5      2.50  3.25  0.372489\n",
       "se  0.843816   0.7  3.3250    1.0      2.55  0.25  0.561001\n",
       "sp  0.650287   6.0  3.3500    1.2      1.30  1.50  0.287985"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price and competition\n",
      "slope: -15.226401    p-value: 0.050218    intercept: 15.858211   R-squared: 0.568573\n",
      "price and regulation\n",
      "slope: -2.665627    p-value: 0.145061    intercept: 5.658085   R-squared: 0.373181\n",
      "price and quality controls\n",
      "slope: -2.062438    p-value: 0.327408    intercept: 2.362447   R-squared: 0.190599\n",
      "price and Management\n",
      "slope: -2.062438    p-value: 0.380990    intercept: 2.362447   R-squared: 0.155727\n",
      "price and Payments\n",
      "slope: -2.062438    p-value: 0.470351    intercept: 2.362447   R-squared: 0.108634\n",
      "markup and competition\n",
      "slope: -2.062438    p-value: 0.532490    intercept: 2.362447   R-squared: 0.082407\n",
      "markup and competition\n",
      "slope: -2.062438    p-value: 0.044453    intercept: 2.362447   R-squared: 0.676541\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "print(\"price and competition\")\n",
    "slopec, interceptc, r_value, p_value, std_err = stats.linregress(tabled.loc[:,'price'], tabled.loc[:,'comp'])\n",
    "print(\"slope: %f    p-value: %f    intercept: %f   R-squared: %f\" % (slopec, p_value, interceptc,r_value**2))\n",
    "\n",
    "print(\"price and regulation\")\n",
    "sloper, interceptr, r_value, p_value, std_err = stats.linregress(tabled.loc[:,'price'], tabled.loc[:,'regu'])\n",
    "print(\"slope: %f    p-value: %f    intercept: %f   R-squared: %f\" % (sloper, p_value, interceptr,r_value**2))\n",
    "\n",
    "print(\"price and quality controls\")\n",
    "slopeq, interceptq, r_value, p_value, std_err = stats.linregress(tabled.loc[:,'price'], tabled.loc[:,'quali'])\n",
    "print(\"slope: %f    p-value: %f    intercept: %f   R-squared: %f\" % (slopeq, p_value, interceptq,r_value**2))\n",
    "\n",
    "print(\"price and Management\")\n",
    "sloped, interceptd, r_value, p_value, std_err = stats.linregress(tabled.loc[:,'price'], tabled.loc[:,'DemManag'])\n",
    "print(\"slope: %f    p-value: %f    intercept: %f   R-squared: %f\" % (slopeq, p_value, interceptq,r_value**2))\n",
    "\n",
    "print(\"price and Payments\")\n",
    "slopep, interceptp, r_value, p_value, std_err = stats.linregress(tabled.loc[:,'price'], tabled.loc[:,'pay'])\n",
    "print(\"slope: %f    p-value: %f    intercept: %f   R-squared: %f\" % (slopeq, p_value, interceptq,r_value**2))\n",
    "\n",
    "print(\"markup and competition\")\n",
    "slopeqm, interceptqm, r_value, p_value, std_err = stats.linregress(tabled.loc[:,'markup'], tabled.loc[:,'comp'])\n",
    "print(\"slope: %f    p-value: %f    intercept: %f   R-squared: %f\" % (slopeq, p_value, interceptq,r_value**2))\n",
    "\n",
    "print(\"markup and competition\")\n",
    "slopeqm1, interceptqm1, r_value, p_value, std_err = stats.linregress(tabled.loc[['dk','fr','it','nl','se','sp'],'markup'], tabled.loc[['dk','fr','it','nl','se','sp'],'comp'])\n",
    "print(\"slope: %f    p-value: %f    intercept: %f   R-squared: %f\" % (slopeq, p_value, interceptq,r_value**2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YmBhEhG3btgUxOlPaZWW5obb/+lfxJ+J77DE30eBrr/kntnBlSb6UuuSSS1BVss/hX6dOHUSEadOmBSkyU5p99RUcO+af5yGio90Q3KefLt3rD1iSL+WqV6+OqpKVlcVll13mLe/duzci4tPC58b4y6uvuputBfXBjxgxgjFjxhRYX8uW0KCBexivtLIkbwAQEWbNmoWqMmnSJG/55MmTERFEhAMHDuR67O+/uwedPvoIpk93872X5isnUzR//gmzZ8P11/u33ptuKt6UGuHOkrzJ4fbbb0dV+TnbiuGVK1dGRFi0aBG//Qb//CfUqwdNm7pJw6ZOhbffdr9UVapAjx7w+edg93SNL9asgfr18171a+TIkTRu3JhLL72U9evXA7Bp0yYuv/xy2rRpQ6dOnVi3bl2O40r9wu/5DaIv6Ze/H4Yy/nHs2DGtXbt2jges2rX7l65cmfuTwgcOqL79tnvgq3Vr1ZUrSz5uE16mTnVPseYmOTlZmzdvrmlpaZqamqrnnnuujh49Wrt27ao///yzqqr+8MMP2qVLlxzHHjqkWrZsICMPLkrDVMMmsGJjY9m2bRu7dik1az7qLf/xxydISBAaNDiX9Gz9MxUrQv/+boWte+6Brl3hzTdLOnITTvJb+H3evHlce+21xMXFUalSJXr16sXRo0dZsGABffr0ITExkbvvvjvHXE5gC79bkjc+2bPHTZh2111Pk5WlzJs3z7tt8+bNxMbGertyTibi5udfsMCNcnj99ZKO3ISLghZ+z77AfVZWFlWqVGH58uXe19q1a3Mct3+/q7u0siRvCqQKd9wBV1zhxi+LwIUXXoiqsi/b/Mnt27dHRPjrX/96SnnDhm5W0MceK+X9oyZP+S383rlzZ6ZPn86RI0c4ePAgn332GXFxcdSvX5+PPvoIcF3PK3KpYMWK0r3wuyV5U6D334ctW9zc9tlVqVLFOwTzZK+99pp3VI565kdq0MDNjX/bbcVbRMVEpnr13Bj5LVtybmvdujU33HADiYmJ9O7dm06dOgHw/vvvM3HiRFq2bEmzZs34JJexkqV94fdSNUGZKbysLGjc2C3I4vm9KtDgwYMZN25cjvJff/2V2rXP5uKLXT/9DTf4N1YT/oYMgUqV4Kmn/FNfZqZbSWz6dGjd2j91hhqboMwUyzffuCFtF17o+zEvv/wy/fv355lsl/516tQhKkpo1Gg8r7zi50BNRBg0yN23KewqanmZOhVq1ozcBO+LgCZ5EdkiIqtEZLmI2CV6GJo5E268sWgzdzZo0ABV5dixY6eUv/nmPcybJ9SuXQrmgTWF0qSJW8v3pHn0imz3brdW84svFr+ucFYSV/JdVDUxvz8nTOhasiT//swtW7bQtGlT7rrrLpo1a0b37t05cuTIKfvExsZ6x+wmJZ34Mdi+/Tdvv31aWlqgmmDCzLPPutFYxZlY7OhRt8zkbbe5pSdLM+uuMfnasMFdXeW/zwbuvfde1qxZQ5UqVfj444/z3Hfx4sWoKhddNOWU8goVKiAi3icZTelVoQJ8+aUbcjt2bOGnCt6zB666Cs48E0aODEyM4STQSV6B/4rIEhEZmNsOIjJQRJJFJDn7jIgm+PJ7QOW4+vXre1eaatOmDVtyGx6RTdOmN/DKK8ru3btPKW/SpAkiwhtvvFHEiE0kaNDALWD/4YfQrZtb6akgWVmuD75FC9cH/+67bibK0i7QSb6jqrYGrgDuFZHO2XdQ1ddVNUlVk+Lj4wMcjimsgh5QASh70qdAdHQ0GRkZBdZ7/AGV+Ph4b1dO//79vdsHDhyIiHinRDalT716bvjj5Ze7G/+XX+7mRlqzxi2ZCXDwIHz3nbtib9AARo+Gjz+GUaMswR8X0CSvqjs8/+4GpgPtAnk+438JCW5WSX9bvjznAypvv/02qsqMGTO8ZbNnzyYqKgoRyXHVbyJfTAw8+CBs2+Ymvps1C665BmJjXRKvUQOGD4ddu9ySgYsXQ4cOwY46tAQsyYtIeRGpePxroDuwOlDnM4HRtq27mvKnPXvgt9/gvPNy33711Vejqvz222+nlNeoUQMR4auvvvJvQCbknXaaW85vyhR3nygz0z04dfgw/PADvPyyu8EaSus3h4qAPQwlIufgrt4BYoDJqprvbRB7GCr0/Pyz+1P511/dL5o/jBoFa9fCW2/5tn9WVhYXXHABP/744ynl9957b64PXRlTmgTtYShV3ayqLT2vZgUleBOaGjWCVq18T8gFSUuDcePcE6++ioqKYtGiRagqL7zwgrf8lVdeQUSIi4vLMWzTGOPYEEpToDFj3AIhv/5a/LoeftjNZlnUuUSGDBmSYyKqI0eOEBcXh4iwcuXK4gdpTASxJG8K1KKFe3KwTx83mqGoJk92a22edDFeZAkJCagqhw8fpsJJSwm1bNkSEeHF0v6YozEeluSNTx5+GBIToXt32LGjcMeqwoQJ8MAD8MUXULWq/+IqV64cBw8eRFX529/+5i0fOnQoIkLbtm1zzJBpTGliSd74RMQl6iuucMn+rbdOjFXOz+bN7unD116DuXOhefPAxfjyyy+jqsyaNctblpycTHR0NCLC9u3bA3dyY0KUJXnjMxHXN//VVzBxolt0ecQImD3bzRqo6oa2rV0L770HPXtCu3bQvr1bBrBx45KJ87LLLkNV+f33308pr127NiKS65zjxkQqS/Km0Fq1gvnz4bPP4MABl/jPPts9nFKmjLty//RT99DKr7/C44+78pJ2xhlneBc06dq1q7f8mmuuQUROecLWmEhli4YYv8nMhKio0H4g5c033+Suu+7KUX7w4MFTbuAaEy5s0RBTYqKjQzvBAwwYMABVzbHgc8WKFRERFi9eHKTIjAkMS/KmVGrSpAmqytGjRznzzDO95e3atUNEcqxqZUy4siRvSrWyZcuyc+dOVJWHHnrIW/7II48gIjRp0sSnWTWNCVWW5I3xePbZZ1FV5s6d6y1bv349ZcqUQUR8miffmFBjSd6YbC666CJUlT/++OOU8vr16yMiTJkyJY8jjQk9luSNyUPVqlW9QzCvvvpqb/mNN96IiHDdddfZgiYm5FmSN6YAIsKMGTNQVd577z1v+fTp070LmuwvaPksY4LEkrwxhdCvXz9UlU2bNp1SXrVqVUSE+fPnBykyY3JnSd6YIjjnnHNQVdLT02nQoIG3vFOnTogIjz32WBCjM+YES/LGFENMTAwbNmxAVXnyySe95SNHjkREqF27Nn/++WcQIzSlXZGTvIjE+jMQY8Ld448/jqqycOFCb9n27dspW7YsIsKGDRuCGJ0prXxK8iIyV0TqnfR9O8Ce/zYmF+3bt0dVSU1NPaW8UaNGiAiTJk0KUmSmNPL1Sv4ZYJaI3CMiI4EJwO2BC8uY8FepUiVUFVWlX79+3vI777wTEaF79+42BNMEnM+zUIrIxcDXwB6glaru8ncwNguliXTTpk2jd+/eOcpTUlKoXr16ECIy4c4vs1CKyOPAy0BnYAQwV0R6+HhstIgsE5HPfdnfmEh2/AGqbdu2nVIeHx+PiPDNN98EKTITqXztrqkOtFPVhar6GnAZMNTHY4cAawvcy5hSpHbt2qgqGRkZtGrVylverVs3RIShQ4cGLzgTUXxK8qo6BEBEGnu+36qq3Qo6TkRqAz2AN4sTpDGRKjo6mqVLl6KqjBkzxlv+4osvIiJUrlyZo0ePBjFCE+587a7pCSwHZnm+TxSRT3049AVgOJCVT90DRSRZRJJTUlJ8CceYiPTAAw+gqixbtsxbduDAAcqVK4eIsHr16iBGZ8KVr901I4B2wH4AVV0O1M/vABG5Ctitqkvy209VX1fVJFVNio+P9zEcYyJXYmIiqkpaWhqxsSceR2nRogUiwrhx44IYnQk3vib5DFVNzVZW0LCcjkAvEdkCTAG6ish7+R9ijDkuLi6OY8eOoarcfffd3vLBgwcjIlxwwQVkZeX5R7IxgO9JfrWI3AREi0hDEXkZWJDfAar6D1Wtrar1gL7AbFW9uXjhGlM6TZgwAVVl5syZ3rIffviB6OhoRISdO3cGMToTynxN8oOBZsAx4APgAL6PrjHG+MmVV16JqrJr16mPqdSqVQsR4bPPPgtSZCZU+Tq65rCqPqqqbT3954+qqs+3/FV1rqpeVfQwjTEnq1GjBqpKZmYmnTt39pb36tULEWHAgAFBjM6EknyfeBWRz8in711Ve/kzGHvi1ZiiGz9+PPfcc88pZVFRURw4cIDy5csHKSoTaMV94nUMMBb4BTgCvOF5HQJsPJcxIWTQoEGoKmvWrPGWZWVlUaFCBUSEpUuXBjE6Eyz5JnlV/Z+q/g83V80NqvqZ53UTcGHJhGiMKYzzzjsPVeXIkSNUq1bNW96mTRtEhFGjRgUxOlPSfL3xGi8i5xz/RkTqAzao3ZgQdtppp7Fnzx5Ulfvvv99b/tBDDyEiJCQkkJmZGcQITUnwNcnfh5uUbK6IzAXmYKNrjAkbY8eORVWZPXu2t2zVqlXExMQgIvz6669BjM4Ekq+ja2YBDXGTjQ0BGqvqV4EMzBjjf126dEFV2bNnzynldevWRUT46KOPghSZCZTCLP/XBjdWviVwg4jcGpiQjDGBVq1aNVSVrKwsevQ4MWv49ddfj4jQt2/fIEZn/MnXCcrexY20uRBo63nlOWTHGBMeRITPP/8cVeWdd97xlk+dOhURQURyLGNowotPK0OJyFrgPA3wWmU2Tt6Y4Nu4cSMNGzbMUf7999/ToUOHIERk8uOXlaFwY+LP9E9IxphQ1qBBA1SVP//8k/r1T0w227FjR0SEESNGBC84U2iFWRnqJxH5SkQ+Pf4KZGDGmOAqU6YMmzdvRlV5/PHHveX/+te/EBHq1atHenp6ECM0vvC1u+ai3Mo9D0r5jXXXGBPaFixYQMeOHXOUb9iwgQYNGgQhIlNQd02ML5X4O5kbY8JThw4dUFVSU1OpUqWKt/x4H/4777zDrbfawLtQkm93jYgcFJEDubwOisiBkgrSGBNaKleujKqiqtxwww3e8v79+yMi3imRTfAVNHdNRVWtlMuroqpWKqkgjTGha8qUKagqH374obfsyy+/JCoqChFh7969QYzOFOZhKGOMyVOfPn1QVbZu3XpKefXq1RER5syZE6TISjdL8sYYv6pTpw6qSkZGBi1atPCWd+3aFRFh2LBhQYyu9LEkb4wJiOjoaFauXImq8txzz3nLx44di4hQrVo1jh07FsQISwdL8saYgBs+fDiqyslDpP/44w9OO+00RIS1a9cGMbrIZkneGFNi2rRpg6py6NAhoqOjveXnnXceIsKECROCGF1ksiRvjClx5cuXJyMjA1Xljjvu8JYPGjQIEaFz5842BNNPApbkReQ0EflRRFaIyBoR+VegzmWMCV8TJ05EVfn00xMzpcybN887BPP3338PYnThL5BX8seArqraEkgELheR9gE8nzEmjPXs2RNVZceOHaeUn3nmmYgIX375ZZAiC28BS/LqHPJ8W8bzsr+/jDH5qlmzJqpKZmYmF1xwgbf8yiuvRERYsmRJEKMLPwHtkxeRaBFZDuwGvlbVRbnsM1BEkkUkOSUlJZDhGGPCSFRUFAsWLEBVefnll73lSUlJ1K9fn5tuusmGYPrAp1koi30SkSrAdGCwqq7Oaz+bhdIYk5/U1FQmTZrE/fff7y2rWbMmCxYsoF69esELLIj8tWhIsajqfmAucHlJnM8YE5kqV67Mfffdx7Fjx7yzXe7cuZP69et7lzI0pwrk6Jp4zxU8IlIOuBRYF6jzGWNKj9jYWN555x1UlUmTJnnLe/bsiYjw0EMPkZWVFcQIQ0cgr+RrAnNEZCWwGNcnbx+zxhi/uv3221FVVqxYgYgAMGrUKKKjo2nXrh1//PFHkCMMrkCOrlmpqq1UNUFVm6vqk4E6lzHGJCQkkJWVRWpqKl27dgVg8eLFVKtWDRHhxx9/DHKEwWFPvBpjIkqlSpX49ttvycrK4qmnnvKWn3/++YgI48aNC2J0Jc+SvDEmIokIjz32GKrKt99+6y0fPHgwIsJ1113HkSNHghhhybAkb4yJeF27dkVV2b59u3c92unTpxMXF8fpp5/Oxo0bgxxh4FiSN8aUGrVq1eLnn38mPT2du+++G4B9+/bRsGFDRISPP/44yBH6nyV5Y0ypExMTw4QJE1BV3n//fW/5X/7yF0SEIUOGkJmZGcQI/ceSvDGmVLvppptQVX766SfKlSsHwEsvvURMTAwJCQns3r07yBEWjyV5Y4wBmjZtyuHDhzl06BA9evQAYNWqVdSoUQMRYf78+UGOsGgsyRtjzEnKly/P559/TlZWFqNHj/aWd+rUCRFh9OjRYbWgiSV5Y4zJhYgwbNgwVJV58+Z5y4cPH05UVBRXXnklaWlpQYzQN5bkjTGmABdeeCGqyu+//05CQgIAX375JRUqVKBcuXIhvRC5JXljjPHRGWecwYoVK8jIyGDIkCEAHD161LsQ+eTJk4McYU6W5I0xppCio6N54YUXUNVTxtb369cPEWHgwIFkZGQEMcITSmTREF/ZoiHGmHC1ceNGzj///FNmvTzrrIY8/PBcTj+9FjVrQuvWULmyf88bEouGGGNMpGvQoAEbNuzlmWcOU7FibwC2b9/A4MFnMXbsSzz2GJx1FiQmwvjxcPBgycRlSd4YY4pJFd56Cxo3hjVryjFr1n/IzDyxNu2wYfF8/z2kpsLYsfDNN9CoEUybFvjYrLvGGGOK4ehRuPlm2LwZ3n4bPINvCvT993D77dClC7z6KkRHF+381l1jjDEBkp4OvXuDCPzwg+8JHqBjR1i6FDZtgjvugECtVmhJ3hhjimjkSJfoJ0+G2NjCH1+hAnzyCaxZA6+/7v/4AGICU60xxkS2FStcN8uyZVCmTNHrKV8e3n0XOneGK66AunX9FyPYlbwxxhTJM8/AI4+4ETPF1bQp3HknPP988evKzpK8McYU0q5d8NVX7sZpfkaMGMGYMWO4+OKLKWhQyaBB7ore39PhBCzJi8jZIjJHRNaKyBoRGRKocxljTEn65hu49FL/PthUty6cdx4sWOC/OiGwV/IZwAOq2hRoD9wrIucF8HzGGFMikpOhbdvct40cOZLGjRtz6aWXsn79+lO2ZWVl0b9/fx577LFcj23bFpYs8W+sAUvyqrpTVZd6vj4IrAX80HtljDHBtWmTe/ApuyVLljBlyhSWLVvGtGnTWLx4sXdbRkYG/fr1o1GjRjz99NO51tukCfh7TfES6ZMXkXpAK2BRLtsGikiyiCSnpKSURDjGGFMs6em5D5mcN28e1157LXFxcVSqVIlevXp5t9199900b96cRx99NM96Y2Nd3f4U8CQvIhWAj4Ghqnog+3ZVfV1Vk1Q1KT4+PtDhGGNMsVWsCPv3575NRHIt79ChA3PmzOHo0aN51rt/P1SqVPz4ThbQJC8iZXAJ/n1VLYFZGowxJvASEmD58pzlnTt3Zvr06Rw5coSDBw/y2WefebfdeeedXHnllfTp0yfPaYiXLSvcU7O+COToGgEmAmtV9d+BOo8xxpS0du3gpBUBvVq3bs0NN9xAYmIivXv3plOnTqdsv//++2ndujW33HILWdnmMVCF+fNd3f4UsAnKRORCYB6wCjjemkdU9Yu8jrEJyowx4SA93Q15/PpraNbMP3XOng1DhsDKlW4uHF8VNEFZwKY1UNX5QCFCNcaY8FCmDAwcCM89B//3f8WvTxWefRbuuadwCd4X9sSrMcYUwbBhrsvmizz7Jnz39tuQkgIDBhS/ruwsyRtjTBFUqACTJrk5Z7I981QoixfD8OEu0RdnorO8WJI3xpgi6tLFTVTWtSssyvEUUMG+/hp69HAfFi1b+j8+sKmGjTGmWG67DapUgauvdl//4x8Fz2mzZw88/jjMnAlTp7oPi0CxK3ljjCmma65x88vv2AH16rmbsh9/DFu2wJ9/utfGjTBlCtxyCzRs6G6wrloV2AQPtsarMcb41c6d8M47bg3XpUth926X0M88E5KS3OIgt9wC1ar553xBG0JpjDGlUc2a8PDDwY7iBOuuMcaYCGZJ3hhjIpgleWOMiWCW5I0xJoJZkjfGmAhmSd4YYyKYJXljjIlgluSNMSaCWZI3xpgIZkneGGMimCV5Y4yJYJbkjTEmglmSN8aYCGZJ3hhjIljAphoWkUnAVcBuVW0eqPMYYyJbaiosWwbbt0NmppuHvVUrqFUr2JGFh0DOJ/82MA74vwCewxgTgQ4fhg8+gAkTYO1aSEhwKy5FRblFOJYuhfLl3SLad93l5nA3uQtYklfV70SkXqDqN8ZEpq++cok7IQGefBK6dYOYbJlKFVaudB8CzZvDI4/A0KEQHR2UkENaQJf/8yT5z/PrrhGRgcBAgDp16rTZunVrwOIxxoQuVXjoIfjwQ3jzTbj0Ut+O27QJ7rjDJfjp0wteRDvSFLT8X9BvvKrq66qapKpJ8fHxwQ7HGBMkDzwA//uf64rxNcEDnHsuzJkDTZrA5ZdDWlrgYgxHQU/yxhjz0UcwcybMmgWnn17446Oi4JVXoH59GDbM//GFM0vyxpig2rMHBg+Gd96BqlWLXo8IvPoqfP45zJ7tv/jCXcCSvIh8ACwEGovIbyJyZ6DOZYwJX+PHw1VXQfv2xa+rShV47jl4+uni1xUpAjm65sZA1W2MiQwZGfDaa+7q21/+8hfXv792LTRt6r96w5V11xhjgmb1aqhYERIT/VdnbCxcdx18+aX/6gxnluSNMUGzZAkk5TH4Ly0tjR49etCyZUuaN2/O1KlTWbJkCRdddBFt2rThsssuY+fOnbkem5Tk6jaBfeLVGGPytWkTNG6c+7ZZs2ZRq1YtZs6cCUBqaipXXHEFn3zyCfHx8UydOpVHH32USZMm5Ti2cWP3oJSxJG+MCaL0dNe9kpsWLVowbNgwHnroIa666iqqVq3K6tWr6datGwCZmZnUzGM+g9hYV7exJG+MCaJKlWD//ty3NWrUiCVLlvDFF1/wj3/8g27dutGsWTMWLlxYYL379rm6jfXJG2OCKCHBzTCZmx07dhAXF8fNN9/MsGHDWLRoESkpKd4kn56ezpo1a3I9dvlyaNkyQEGHGbuSN8YETdu2bt6ZY8egbNlTt61atYoHH3yQqKgoypQpw/jx44mJieHvf/87qampZGRkMHToUJo1a5aj3nnz4PrrS6gRIS6gE5QVVlJSkiYnJwc7DGNMCbrkEhgwAG7005M127dDixawdasbnhnpQn6CMmNM6XbvvTB6tHswyh/GjHEfGKUhwfvCkrwxJqiuvdZNSjZ2bPHrWrgQpkyBESOKX1eksD55Y0xQicDEia5/vlUr6N69aPVs2+au4F95BWzW8hPsSt4YE3R167oFP26+GaZOLfzxK1dC584wZIib0sCcYEneGBMSOnZ088n/85/Qt6+7Mi/IoUNuicBLLoGnnoL77gt8nOHGkrwxJmS0bu3GuNev78a5X3utm2d+9Wq3uPfRo27UzLRp7oZtnTqwapVbTermm4MdfWiyIZTGmJB08CB88IFb2m/JEvjtN8jMhGrV3IdB+/bQvz+cfXawIw2ugoZQWpI3xpgwZuPkjTGmFAupK3kRSQG2+rBrdWBPgMMpSZHWHoi8Nll7Ql+ktcnX9tRV1TwHjYZUkveViCTn9+dJuIm09kDktcnaE/oirU3+ao911xhjTASzJG+MMREsXJP868EOwM8irT0QeW2y9oS+SGuTX9oTln3yxhhjfBOuV/LGGGN8YEneGGMiWEgleRG5XETWi8hGEXk4j30uFpHlIrJGRP5XmGODoZht2iIiqzzbQuJR4ILaIyIPeuJdLiKrRSRTRE735dhgKGZ7Qu79AZ/aVFlEPhORFZ6fudt9PTYYitmekHuPfGhPVRGZLiIrReRHEWnu67G5UtWQeAHRwCbgHCAWWAGcl22fKsBPQB3P92f4emy4tcnz9RagerDbUZj2ZNu/JzA7VN+j4rQnFN+fQvzMPQI85/k6HvjDs29Yvkd5tScU3yMf2zMaeMLzdRPg26L8vB5/hdKVfDtgo6puVtU/gSnA1dn2uQmYpqq/Aqjq7kIcGwzFaVMoKuz/843AB0U8tiQUpz2hypc2KVBRRASogEuKGT4eW9KK055Q5Et7zgO+BVDVdUA9Eanh47E5hFKSPws4eQbp3zxlJ2sEVBWRuSKyRERuLcSxwVCcNoH74f2vp3xggGP1hc//zyISB1wOfFzYY0tQcdoDoff+gG9tGgc0BXYAq4Ahqprl47ElrTjtgdB7j3xpzwrgOgARaQfUBWr7eGwOobT8n+RSln18ZwzQBrgEKAcsFJEffDw2GIrcJlX9GeioqjtE5AzgaxFZp6rfBTbkfBXm/7kn8L2q/lGEY0tKcdoDoff+gG9tugxYDnQFzsXFPs/HY0takdujqgcIvffIl/Y8C7woIstxH1rLcH+ZFOn9CaUr+d+Ak2eGro37ZM6+zyxVTVPVPcB3QEsfjw2G4rQJVd3h+Xc3MB3351owFeb/uS+ndm2E4ntUnPaE4vsDvrXpdlwXoarqRuAXXN9vuL5HebUnFN+jAtujqgdU9XZVTQRuxd1n+MWXY3MV7BsRJ91siAE2A/U5cVOhWbZ9muL6qmKAOGA10NyXY8OwTeWBip59ygMLgMtDvT2e/Srj+kXLF/bYMGpPyL0/hfiZGw+M8HxdA9iOm/EwLN+jfNoTcu+Rj+2pwokbx3cB/1eYn9cc5wz2D2W2xl0J/Iy7g/yop+yvwF9P2udB3GiU1cDQ/I4NhVdR24S7g77C81oTKm3ysT23AVN8OTbYr6K2J1TfH1/aBNQC/ovrClgN3BzO71Fe7QnV98iH9lwAbADWAdOAqsV5f2xaA2OMiWCh1CdvjDHGzyzJG2NMBLMkb4wxEcySvDHGRDBL8sYYE8EsyZuwIiL1RGS1n+q6TUTGeb6+RkTOO2nbXBHx26LQIvLmyfUbU1IsyRvjXIObGMrvRCRaVQeo6k+BqN+Y/FiSN+EoWkTe8Mwd/l8RKQcgIueKyCzPZFTzRKSJp7yniCwSkWUi8o1nRj8vEekA9AJGe+YdP9ezqY9nPu+fRaRT9iDErQPwnWfu759EZIKIRHm2HRKRJ0VkEXDByX8ZeOYEX+qZ//xbT1l5EZkkIos9cQZ79kcTISzJm3DUEHhFVZsB+4HenvLXgcGq2gYYBrzqKZ8PtFfVVrjpWYefXJmqLgA+BR5U1URV3eTZFKOq7YChwBN5xNIOeABogZsc6zpPeXlgtaqer6rzj+8sIvHAG0BvVW0J9PFsehQ3V31boAvuA6e87/8lxuQulGahNMZXv6jqcs/XS3DzbVcAOgAfuWnFASjr+bc2MFVEauLm/PjFx/NMO/kceezzo6puBhCRD4ALgf8AmZw6LfFx7YHvVPUXAD0xq2V3oJeIDPN8fxpQB1jrY6zG5MqSvAlHx076OhM3RXMUsF/dzH3ZvQz8W1U/FZGLgRGFPE8mef+uZJ8X5Pj3R1U1M5f9JZdjjpf3VtX1PsZmjE+su8ZEBHVzh/8iIn0AxGnp2VwZNzMhQP88qjgIVCzCqduJSH1PX/wNuK6h/CwELhKR+p44T/eUfwUM9qxuhIi0KkIsxuRgSd5Ekn7AnSJyfNbB4zcvR+C6ceYBe/I4dgrwoOem57l57JObhbhFHlbjuoGm57ezqqYAA4FpnjinejY9BZQBVnqGiD5ViBiMyZPNQmlMEXm6foap6lVBDsWYPNmVvDHGRDC7kjfGmAhmV/LGGBPBLMkbY0wEsyRvjDERzJK8McZEMEvyxhgTwf4fJcXLGxw33nUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(tabled['price'],tabled['comp'],facecolors='none', edgecolors='b',s=500.0)\n",
    "#sns.regplot(tabled['price'],tabled['comp'], ci=95, color='none', marker=\" \")#color ='blue', marker=\"o\", label=countries)\n",
    "plt.plot(tabled['price'], interceptc + slopec*tabled['price'], 'k')\n",
    "plt.xlim(0.59,0.91)\n",
    "plt.ylim(0.25,7.5)\n",
    "plt.xlabel('health price')\n",
    "plt.ylabel('Index')\n",
    "for x,y,z in zip(tabled['price'],tabled['comp'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n",
    "plt.savefig('../figures/fig2_comp_price.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tabled['price'],tabled['regu'],facecolors='none', edgecolors='b',s=500.0)\n",
    "#sns.regplot(tabled['price'],tabled['regu'], color='none', marker=\" \")#color ='blue', marker=\"o\", label=countries)\n",
    "plt.plot(tabled['price'], interceptr + sloper*tabled['price'], 'k')\n",
    "plt.xlabel('health price')\n",
    "plt.ylabel('Index')\n",
    "#plt.xlim(0.59,0.91)\n",
    "#plt.ylim(2.9,4.8)\n",
    "plt.xlim(0.59,0.91)\n",
    "plt.ylim(3.2,4.5)\n",
    "for x,y,z in zip(tabled['price'],tabled['regu'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n",
    "plt.savefig('../figures/fig2_reg_price.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tabled['price'],tabled['quali'],facecolors='none', edgecolors='b',s=500.0)\n",
    "#sns.regplot(tabled['price'],tabled['quali'], color='none', marker=\" \")#color ='blue', marker=\"o\", label=countries)\n",
    "plt.plot(tabled['price'], interceptq + slopeq*tabled['price'], 'k')\n",
    "plt.xlabel('health price')\n",
    "plt.ylabel('Index')\n",
    "plt.xlim(0.59,0.91)\n",
    "plt.ylim(-0.5,1.9)\n",
    "for x,y,z in zip(tabled['price'],tabled['quali'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n",
    "plt.savefig('../figures/fig2_qual_price.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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